Addressing User Cold Start Problem in Amharic YouTube Advertisement Recommendation Using BERT
| dc.contributor.advisor | Fitsum Assamnew (PhD) | |
| dc.contributor.author | Firehiwot Kebede | |
| dc.date.accessioned | 2024-07-31T08:22:53Z | |
| dc.date.available | 2024-07-31T08:22:53Z | |
| dc.date.issued | 2024-06 | |
| dc.description.abstract | With the rapid growth of the internet and smart mobile devices, online advertising has become widely accepted across various social media platforms. These platforms employ recommendation systems to personalize advertisements for individual users. However, a significant challenge for these systems is the user cold-start problem, where recommending items to new users is difficult due to the lack of historical preference of the user in a content-based recommendation system. To address this issue we propose an Amharic YouTube advertisement recommendation system for unsigned YouTube users where there is no user information like past preference or personal information. The proposed system uses content-based filtering techniques and leverages Sentence Bidirectional Encoder Representations from Transformers (SBERT) to establish sentence semantic similarity between YouTube video titles, descriptions, and advertisement titles. For this research, 4500 data were collected and preprocessed from YouTube via YouTube API, and 500 advertisement titles from advertising and promotional companies. Random samples from these datasets were annotated for evaluation purposes. Our proposed approach achieved a 70% accuracy in recommending semantically related Amharic Advertisements (Ads) to corresponding YouTube videos with respect to the annotated data. At a 95% confidence interval, our system demonstrated an accuracy of 58% to 76% in recommending Ads which are relevant to new users who have no prior interaction history on the platform with the Ads. This approach significantly enhances privacy by reducing the need for extensive data sharing. | |
| dc.identifier.uri | https://etd.aau.edu.et/handle/123456789/3317 | |
| dc.language.iso | en_US | |
| dc.publisher | Addis Ababa University | |
| dc.subject | user cold-start | |
| dc.subject | Content-based filtering | |
| dc.subject | BERT | |
| dc.subject | Online advertisement | |
| dc.subject | recommendation system | |
| dc.title | Addressing User Cold Start Problem in Amharic YouTube Advertisement Recommendation Using BERT | |
| dc.type | Thesis |